AI 中文总结
针对真实数据标签噪声问题，提出VanillaSort，结合多通道检测与模板引导聚类，在Hybrid Janelia上提升检测与分类性能。
AI 中文摘要
在真实记录上训练尖峰检测器具有挑战性，因为算法生成的标签可能带有噪声且不完整。我们提出了VanillaSort，它将多通道检测与空间增强的、模板引导的聚类相结合。VanillaDet使用可见性感知掩蔽、截断高斯目标和时间容忍的正包损失，随后进行条件事件信噪比门控。VanillaCluster将HuiduRep嵌入与相对幅度特征结合用于高斯混合聚类，并使用从选定的核心事件构建的交叉拟合波形模板来优化分配。VanillaDet在Hybrid Janelia的静态和漂移子集上，检测准确率分别比SimSort提高了两个和三个百分点。完整的流程也相对于相应的HuiduRep基线提高了分类性能。这些结果支持从不完美的真实数据标签中学习，并将波形一致性纳入神经元分配。
英文摘要
Training spike detectors on real recordings is challenging because algorithmically generated labels can be noisy and incomplete. We propose VanillaSort, combining multichannel detection with spatially augmented, template-guided clustering. VanillaDet uses visibility-aware masking, truncated Gaussian targets and a temporally tolerant positive-bag loss, followed by conditional event-SNR gating. VanillaCluster combines HuiduRep embeddings with relative-amplitude features for Gaussian mixture clustering and refines assignments using cross-fitted waveform templates built from selected core events. VanillaDet improves detection accuracy over SimSort by two and three percentage points on the static and drift subsets of Hybrid Janelia, respectively. The complete pipeline also improves sorting performance over the corresponding HuiduRep baselines. These results support learning from imperfect real-data labels and incorporating waveform consistency into neuronal assignment.
Comments5 pages, 3 figures, 3 tables
